Mautic → Chartio
AI-first ETL from Mautic into Chartio. Governed entities, incremental sync, typed landing tables.
How Datrise loads Mautic into Chartio
Datrise syncs Mautic's contacts, accounts, deals, activities, and lifecycle events into Chartio as SQL tables a visual-SQL explorer connects to. Flexible or custom fields land in flattened columns for visual SQL, and timestamps such as created, updated, and status changes are typed as temporal columns.
Sync is incremental: Datrise uses incremental refresh of the connected tables, so re-runs update only what changed. Date-partitioned facts. Visual-SQL tools build joins from your schema, so Datrise lands clearly related tables with stable id columns.
Ideal for drag-and-drop charting over a database.
Endpoints
Mautic: Open-source CRM for customizable sales and customer workflows.
Chartio: Cloud BI for exploring warehouse data with drag-and-drop charts.
How Mautic entities map to Chartio
| Mautic entity | Chartio object | Notes |
|---|---|---|
| contacts | mautic_contacts | id PK · custom fields → flattened columns for visual SQL |
| accounts | mautic_accounts | id PK · linked to mautic_contacts |
| deals | mautic_deals | id PK · linked to mautic_contacts |
| activities | mautic_activities | temporal columns events |
FAQ
How does Datrise handle Mautic's custom fields in Chartio?
Flexible values are stored as flattened columns for visual SQL, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Chartio types.
How does the Mautic to Chartio sync stay up to date?
It runs incrementally — Datrise uses incremental refresh of the connected tables.
Related pipelines
More destinations for Mautic
Early access
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